Music source separation, i.e., separating the sources (instruments) involved in an audio recording, has been a major research topic in the signal processing community, partly due to its wide downstream applications in music upmixing and remixing, karaoke, DJ-related applications, and as a pre-processing tool for other problems [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}.
Its technical difficulty has also been well acknowledged. For example,
the estimation of the number of sources involved in a song remains challenging [17]}. Even when the number of sources is known or given beforehand, in supervised training we need the multi-track recordings that provide the ground truth single-instrument tracks (a.k.a., `stems') that compose a song. Such multi-track recordings are rarely publicly available due to copyright issues [18]}, [19]}. What are typically available instead are the mixed versions of the songs, where the multiple tracks have been mixed and combined into a monaural (i.e., one-channel) or stereo (two-channel) recording. The lack of available data with ground truth stems not only limits the development of data-driven methods, but also hinders systematic evaluation of new methods proposed for the task.
